{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/fake_news.csv', parse_dates=['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(sec:fake_news_exploring)=\n",
    "# Exploring the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset of news articles we're exploring is just one part of the larger FakeNewsNet dataset. As such, the original paper doesn't provide detailed information about our subset of data.\n",
    "So, to better understand the data, we must explore it ourselves.\n",
    "\n",
    "Before starting exploratory data analysis, we apply our standard practice of splitting the data into training and test sets. We perform EDA using only the train set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df['label'] = (df['label'] == 'fake').astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df[['timestamp', 'baseurl', 'content']], df['label'],\n",
    "    test_size=0.25, random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>baseurl</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>2019-01-04 19:25:46</td>\n",
       "      <td>worldnewsdailyreport.com</td>\n",
       "      <td>Chinese lunar rover finds no evidence of Ameri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-01-12 21:02:28</td>\n",
       "      <td>occupydemocrats.com</td>\n",
       "      <td>Virginia Republican Wants Schools To Check Chi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              timestamp                   baseurl   \n",
       "164 2019-01-04 19:25:46  worldnewsdailyreport.com  \\\n",
       "28  2016-01-12 21:02:28       occupydemocrats.com   \n",
       "\n",
       "                                               content  \n",
       "164  Chinese lunar rover finds no evidence of Ameri...  \n",
       "28   Virginia Republican Wants Schools To Check Chi...  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's count the number of real and fake articles in the train set: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    320\n",
       "1    264\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our train set has 584 articles, and there are about 60 more articles labeled as `real` compared to `fake`. Next, we check for missing values in the three fields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 584 entries, 164 to 102\n",
      "Data columns (total 3 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   timestamp  306 non-null    datetime64[ns]\n",
      " 1   baseurl    584 non-null    object        \n",
      " 2   content    584 non-null    object        \n",
      "dtypes: datetime64[ns](1), object(2)\n",
      "memory usage: 18.2+ KB\n"
     ]
    }
   ],
   "source": [
    "X_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearly half of the timestamps are null. This feature will limit the dataset if we use it in the analysis. Let's take a closer look at the `baseurl`, which represents the website that published the original article."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Publishers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand the `baseurl` column, we start by counting the number of articles from each website:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseurl\n",
       "whitehouse.gov               21\n",
       "abcnews.go.com               20\n",
       "nytimes.com                  17\n",
       "                             ..\n",
       "occupydemocrats.com           1\n",
       "legis.state.ak.us             1\n",
       "dailynewsforamericans.com     1\n",
       "Name: count, Length: 337, dtype: int64"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['baseurl'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our train set has 584 rows, and we have found that there are 337 unique publishing websites. This means that the dataset includes many publications with only a few articles. A histogram of the number of articles published by each website confirms this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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   ],
   "source": [
    "fig=px.histogram(X_train['baseurl'].value_counts(), width=450, height=250,\n",
    "            labels={\"value\":\"Number of articles published at a URL\"})\n",
    "\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This histogram shows that the vast majority (261 out of 337) of websites have only one article in the train set, and only a few websites have more than five articles in the train set.\n",
    "Nonetheless, it can be informative to identify the websites that published the most fake or real articles. First, we find the websites that published the most fake articles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   },
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   ],
   "source": [
    "top_fake_publishers = (\n",
    "    X_train.assign(label=y_train)\n",
    "    .query(\"label == 1\")\n",
    "    [\"baseurl\"]\n",
    "    .value_counts()\n",
    "    .iloc[:10]\n",
    "    .sort_values()\n",
    ")\n",
    "\n",
    "fig = px.bar(\n",
    "    top_fake_publishers,\n",
    "    orientation=\"h\", width=550, height=250,\n",
    "    labels={\"value\": \"Number of articles published at a URL\", \n",
    "            \"index\": \"Base URL\"},\n",
    ")\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we list the websites that published the greatest number of real articles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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    }
   ],
   "source": [
    "top_real_publishers = (\n",
    "    X_train.assign(label=y_train)\n",
    "    .query(\"label == 0\")\n",
    "    [\"baseurl\"]\n",
    "    .value_counts()\n",
    "    .iloc[:10]\n",
    "    .sort_values()\n",
    ")\n",
    "\n",
    "fig = px.bar(\n",
    "    top_real_publishers,\n",
    "    orientation=\"h\", width=550, height=250,\n",
    "    labels={\"value\": \"Number of articles published at a URL\",\n",
    "            \"index\": \"Base URL\"},\n",
    ")\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only `cnn.com` appears on both lists. Even without knowing the total number of articles for these sites, we might expect that an article from `yournewswire.com` is more likely to be labeled as `fake`, while an article from `whitehouse.gov` is more likely to be labeled as `real`. That said, we don't expect that using the publishing website to predict article truthfulness would work very well; there are simply too few articles from most of the websites in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's explore the `timestamp` column, which records the publication date of the news articles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Publication Date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the timestamps on a histogram shows that most articles were published after 2000, although there seems to be at least one article published before 1940:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(\n",
    "    X_train[\"timestamp\"],\n",
    "    labels={\"value\": \"Publication year\"}, width=550, height=250,\n",
    ")\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we take a closer look at the new articles published prior to 2000, we find that the timestamps don't match the actual publication date of the article. These date issues are most likely related to the web scraper collecting inaccurate information from the web pages. We can zoom into the region of the histogram after 2000:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(\n",
    "    X_train.loc[X_train[\"timestamp\"] > \"2000\", \"timestamp\"],\n",
    "    labels={\"value\": \"Publication year\"}, width=550, height=250, \n",
    ")\n",
    "fig.update_layout(showlegend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, most of the articles were published between 2007 (the year Politifact was founded) and 2020 (the year the FakeNewsNet repository was published). But we also find that the timestamps are concentrated on the years 2016 to 2018—the year of the controversial 2016 US presidential election and the two years following. This insight is a further caution on the limitation of our analysis to carry over to nonelection years. \n",
    "\n",
    "Our main aim is to use the text content for classification. We explore some word frequencies next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Words in Articles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'd like to see whether there's a relationship between the words used in the articles and whether the article was labeled as `fake`. One simple way to do this is to look at individual words like _military_, then count how many articles that mentioned \"military\" were labeled `fake`. For _military_ to be useful, the articles that mention it should have a much higher or much lower fraction of fake  articles than 45% (the proportion of fake articles in the dataset: 264/584). \n",
    "\n",
    "We can use our domain knowledge of political topics to pick out a few candidate words to explore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "word_features = [\n",
    "    # names of presidential candidates\n",
    "    'trump', 'clinton',\n",
    "    #congress words\n",
    "    'state', 'vote', 'congress', 'shutdown',\n",
    "    \n",
    "    # other possibly useful words\n",
    "    'military', 'princ', 'investig', 'antifa', \n",
    "    'joke', 'homeless', 'swamp', 'cnn', 'the'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we define a function that creates a new feature for each word, where the feature contains `True` if the word appeared in the article and `False` if not: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_word_features(df, words):\n",
    "    features = { word: df['content'].str.contains(word) for word in words }\n",
    "    return pd.DataFrame(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is like one-hot encoding for the presence of a word (see {numref}`Chapter %s <ch:linear>`). We can use this function to further wrangle our data and create a new data frame with a feature for each of our chosen words:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_words = make_word_features(X_train, word_features)\n",
    "df_words[\"label\"] = df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(584, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_words.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
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       "<p>4 rows × 16 columns</p>\n",
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      "text/plain": [
       "     trump  clinton  state   vote  ...  swamp    cnn   the  label\n",
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    "Now we can find the proportion of these articles that were labeled `fake`. We visualize these calculations in the following plots. In the left plot, we mark the proportion of `fake` articles in the entire train set using a dotted line, which helps us understand how informative each word feature is—a highly informative word will have a point that lies far away from the line:"
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   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "hide-input"
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   "source": [
    "fake_props = (make_word_features(X_train, word_features)\n",
    " .assign(label=(y_train == 1))\n",
    " .melt(id_vars=['label'], var_name='word', value_name='appeared')\n",
    " .query('appeared == True')\n",
    " .groupby('word')\n",
    " ['label']\n",
    " .agg(['mean', 'count'])\n",
    " .rename(columns={'mean': 'prop_fake'})\n",
    " .sort_values('prop_fake', ascending=False)\n",
    " .reset_index()\n",
    " .melt(id_vars='word')\n",
    ")\n",
    "\n",
    "g = sns.catplot(data=fake_props, x='value', y='word', col='variable',\n",
    "                s=5, jitter=False, sharex=False, height=3)\n",
    "\n",
    "[[prop_ax, _]] = g.axes\n",
    "prop_ax.axvline(0.45, linestyle='--')\n",
    "prop_ax.set(xlim=(-0.05, 1.05))\n",
    "\n",
    "titles = ['Proportion of articles marked fake', 'Number of articles with word']\n",
    "\n",
    "for ax, title in zip(g.axes.flat, titles):\n",
    "    # Set a different title for each axes\n",
    "    ax.set(title=title)\n",
    "    ax.set(xlabel=None)\n",
    "    ax.set(ylabel=None)\n",
    "    ax.yaxis.grid(True);"
   ]
  },
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   "source": [
    "This plot reveals a few interesting considerations for modeling.\n",
    "For example, notice that the word _antifa_ is highly predictive---all articles that mention the word _antifa_ are labeled `fake`. However, _antifa_ only appears in a few articles. On the other hand, the word _the_ appears in nearly every article, but is uninformative for distinguishing between `real` and `fake` articles because the proportion of articles with _the_ that are fake matches the proportion of fake articles overall. We might instead do better with a word like _vote_, which is predictive and appears in many news articles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This exploratory analysis brought us understanding of the time frame that our news articles were published, the broad range of publishing websites captured in the data, and candidate words to use for prediction. Next, we fit models for predicting whether articles are fake or real."
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
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